Mengatasi Outlier dengan Metode Least Trimmed Squares (Lts) pada Regresi Robust

dc.contributor.advisor	Harahap, Marwan
dc.contributor.advisor	Sebayang, Djakaria
dc.contributor.author	Mardhiah, I’syatun
dc.date.accessioned	2022-12-29T03:30:03Z
dc.date.available	2022-12-29T03:30:03Z
dc.date.issued	2011
dc.identifier.uri	https://repositori.usu.ac.id/handle/123456789/78934
dc.description.abstract	This study is to get a regression equation better than regression equation before for data have outlier. First, check outlier at data, with grafic and looking for residu studenization, leverage value, DfFitS, DfBETAS(s) and Cook’s Distance. And then searching regression equation with Least Trimmed Squares (LTS) method at robust regression, that is with get total of sum minimum kuadrat residu with coverage measured. It will get regression equation with LTS method better than equation before with OLS because LTS can make outlier influence be smaller than before for data.	en_US
dc.language.iso	id	en_US
dc.publisher	Universitas Sumatera Utara	en_US
dc.title	Mengatasi Outlier dengan Metode Least Trimmed Squares (Lts) pada Regresi Robust	en_US
dc.type	Thesis	en_US
dc.identifier.nim	NIM070823027
dc.identifier.nidn	NIDN0025124602
dc.identifier.nidn	NIDN0027125103
dc.identifier.kodeprodi	KODEPRODI44201#Matematika
dc.description.pages	49 Halaman	en_US
dc.description.type	Skripsi Sarjana	en_US